Advertisement

Aspectual Classifications: Use of Raters’ Associations and Co-occurrences of Verbs for Aspectual Classification in German

  • Michael RichterEmail author
  • Jürgen Hermes
  • Claes Neuefeind
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11352)

Abstract

The present study examines the results of experiments on the automatic classification of German verbs into five aspectual classes [1]: An experiment within an unsupervised framework based on associations of raters [1] and a couple of experiments within a distributional framework, i.e. in window-based and in a subcategorization-frame-based approach [2]. We compare the predictive power of raters’ associations against two types of verbal cooccurrences: i. pure, unstructured co-occurrences and ii. linguistically motivated, well defined co-occurrences which we denote as informed distributional framework. We observed substantial (unsupervised) and excellent (supervised) agreements with a Gold Standard classification.

Keywords

Machine learning Classification Aspectual verb classes Unsupervised learning Supervised learning 

References

  1. 1.
    Richter, M., van Hout, R.: A classification of German verbs using empirical language data and concepts of Vendler and Dowty. Sprache und Datenverarbeitung (Int. J. Lang. Data Process.) 38, 81–117 (2016)Google Scholar
  2. 2.
    Hermes, J., Richter, M., Neuefeind, C.: Supervised classification of aspectual verb classes in German: subcategorization-frame-based vs window-based approach: a comparison. In: Proceedings of 10th International Conference on Agents and Artificial Intelligence, ICAART 2018, pp. 653–662 (2018)Google Scholar
  3. 3.
    Harris, Z.: Distributional structure. Word 10, 146–162 (1954)CrossRefGoogle Scholar
  4. 4.
    Rubenstein, H., Goodenough, J.B.: Contextual correlates of synonymy. Commun. ACM 8, 627–633 (1965)CrossRefGoogle Scholar
  5. 5.
    Schütze, H., Pedersen, J.: A vector model for syntagmatic and paradigmatic relatedness, pp. 104–113 (1993)Google Scholar
  6. 6.
    Landauer, T.K., Dumais, S.T.: A solution to Plato’s problem: the latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychol. Rev. 104, 211–240 (1997)CrossRefGoogle Scholar
  7. 7.
    Pantel, P.: Inducing ontological co-occurrence vectors. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, ACL 2005, Stroudsburg, PA, USA, pp. 125–132. Association for Computational Linguistics (2005)Google Scholar
  8. 8.
    Turney, P.D., Pantel, P.: From frequency to meaning: vector space models of semantics. J. Artif. Int. Res. 37, 141–188 (2010)MathSciNetzbMATHGoogle Scholar
  9. 9.
    Vendler, Z.: Linguistics in philosophy: G - Reference. Information and Interdisciplinary Subjects Series. Cornell University Press, Ithaca (1967)Google Scholar
  10. 10.
    Schulte im Walde, S., Melinger, A.: Identifying semantic relations and functional properties of human verb associations. In: Proceedings of the Joint Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 612–619 (2005)Google Scholar
  11. 11.
    Schulte im Walde, S.: Human verb associations as the basis for gold standard verb classes: validation against GermaNet and FrameNet. In: Proceedings of the 5th Conference on Language Resources and Evaluation, pp. 825–830 (2006)Google Scholar
  12. 12.
    Schulte im Walde, S.: Can human verb associations help identify salient features for semantic verb classification? In: Proceedings of the 10th Conference on Computational Natural Language Learning, pp. 69–76 (2006)Google Scholar
  13. 13.
    Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998).  https://doi.org/10.1007/BFb0026683CrossRefGoogle Scholar
  14. 14.
    Richter, M., Hermes, J.: Classification of German verbs using nouns in argument positions and aspectual features. In: NetWordS 2015 Word Knowledge and Word Usage, pp. 177–181 (2015)Google Scholar
  15. 15.
    Hermes, J., Richter, M., Neuefeind, C.: Automatic induction of German aspectual verb classes in a distributional framework. In: Proceedings of the International Conference of the German Society for Computational Linguistics and Language Technology, (GSCL 2015), pp. 122–129 (2015)Google Scholar
  16. 16.
    Næss, A. (ed.): Prototypical Tranisitivity: Typological Studies in Language 72. Benjamins, Amsterdam (2007)Google Scholar
  17. 17.
    Siegel, E.V., McKeown, K.R.: Learning methods to combine linguistic indicators: improving aspectual classification and revealing linguistic insights. Comput. Linguist. 26, 595–628 (2000)CrossRefGoogle Scholar
  18. 18.
    Krifka, M.: Nominal reference, temporal constitution, and quantification in event semantics. In: Bartsch, R., van Benthem, J., van Emde Boas, P. (eds.) Semantics and Contextual Expressions, pp. 75–115. Foris (1989)Google Scholar
  19. 19.
    Van Orman Quine, W.: Word and Object. MIT press, Cambridge (1960)zbMATHGoogle Scholar
  20. 20.
    Tomasello, M.: Do young children have adult syntactic competence? Cognition 74, 209–253 (2000)CrossRefGoogle Scholar
  21. 21.
    Goldberg, A.E.: Constructions: A Construction Grammar Approach to Argument Structure. University of Chicago Press, Chicago (1995)Google Scholar
  22. 22.
    Naigles, L.G., Fowler, A., Helm, A.: Developmental shifts in the construction of verb meanings. Cogn. Dev. 7, 403–427 (1992)CrossRefGoogle Scholar
  23. 23.
    Naigles, L., Fowler, A., Helm, A.: Syntactic bootstrapping from start to finish with special reference to down syndrome. In: Beyond Names for Things: Young Children’s Acquisition of Verbs, pp. 299–330 (1995)Google Scholar
  24. 24.
    Naigles, L., Gleitman, L., Gleitman, H.: Children acquire word meaning components from syntactic evidence. In: Language and Cognition: A Developmental Perspective, Norwood, NJ, Ablex, vol. 5, pp. 104–140 (1993)Google Scholar
  25. 25.
    Wittek, A.: Learning the Meaning of Change-of-State Verbs: A Case Study of German Child Language, vol. 17. Walter de Gruyter, Berlin (2002)CrossRefGoogle Scholar
  26. 26.
    Richter, M., van Hout, R.: Interpreting resultative sentences in German: stages in L1 acquisition. Linguistics 51, 117–144 (2013)CrossRefGoogle Scholar
  27. 27.
    Dowty, D.: Word Meaning and Montague Grammar. D. Reidel, Dordrecht (1979)CrossRefGoogle Scholar
  28. 28.
    Dowty, D.: Thematic proto-roles and argument selection. Language 67, 547–619 (1991)CrossRefGoogle Scholar
  29. 29.
    Rothstein, S.: Structuring Events: A Study in the Semantics of Aspect. Explorations in Semantics. Wiley, Hoboken (2004)CrossRefGoogle Scholar
  30. 30.
    Fernando, T.: A finite-state approach to events in natural language semantics. J. Log. Comput. 14, 79–92 (2004)MathSciNetCrossRefGoogle Scholar
  31. 31.
    Gruender, S.: An algorithm from adverbial aspect shift. In: Proceedings of the 22nd International Conference on Computer Linguistics (Coling 2008), pp. 289–296 (2008)Google Scholar
  32. 32.
    Klein, W.: How time is encoded. In: The Expression of Time, pp. 39–82. Mouton de Gruyter, Berlin (2009)Google Scholar
  33. 33.
    Siegel, E.V.: Learning methods for combining linguistic indicators to classify verb. In: Proceedings of the 2nd Conference on Empirical Methods in Natural Language Processing, EMNLP. Brown University, Providence (1997). cmp-lg/9707015Google Scholar
  34. 34.
    Klavans, J.L., Chodorow, M.: Degrees of stativity: the lexical representation of verb aspect. In: Proceedings of the 14th Conference on Computational Linguistics, COLING 1992, Stroudsburg, PA, USA, vol. 4, pp. 1126–1131. Association for Computational Linguistics (1992)Google Scholar
  35. 35.
    Zarcone, A., Lenci, A.: Computational models of event type classification in context. In: Proceedings of the 6th International Conference on Language Resources and Evaluation, Marrakech, Morocco, pp. 1232—1238 (2008)Google Scholar
  36. 36.
    Friedrich, A., Palmer, A.: Automatic prediction of aspectual class of verbs in context. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Baltimore, Maryland, 517–523. Association for Computational Linguistics(2014)Google Scholar
  37. 37.
    Dorr, B.J., Jones, D.A.: Role of word sense disambiguation in lexical acquisition: predicting semantics from syntactic cues. In: Proceedings of the 16th International Conference on Computational Linguistics, COLING 1996, 5–9 August 1996, pp. 322–327. Center for Sprogteknologi, Copenhagen (1996)Google Scholar
  38. 38.
    Merlo, P., Stevenson, S.: Automatic verb classification based on statistical distributions of argument structure. Comput. Linguist. 27, 373–408 (2001)CrossRefGoogle Scholar
  39. 39.
    Joanis, E., Stevenson, S., James, D.: A general feature space for automatic verb classification. Natural Lang. Eng. 14, 337–367 (2008)CrossRefGoogle Scholar
  40. 40.
    Vlachos, A., Korhonen, A., Ghahramani, Z.: Unsupervised and constrained Dirichlet process mixture models for verb clustering. In: Proceedings of the Workshop on Geometrical Models of Natural Language Semantics, GEMS 2009, Stroudsburg, PA, USA, pp. 74–82. Association for Computational Linguistics (2009)Google Scholar
  41. 41.
    Schulte im Walde, S., Brew, C.: Inducing German semantic verb classes from purely syntactic subcategorisation information. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, ACL 2002, Stroudsburg, PA, USA, pp. 223–230. Association for Computational Linguistics (2002)Google Scholar
  42. 42.
    Schulte im Walde, S.: Experiments on the choice of features for learning verb classes. In: Proceedings of the Tenth Conference on European Chapter of the Association for Computational Linguistics, EACL 2003, Stroudsburg, PA, USA, vol. 1, pp. 315–322. Association for Computational Linguistics (2003)Google Scholar
  43. 43.
    Schulte im Walde, S.: Experiments on the automatic induction of German semantic verb classes. Comput. Linguist. 32, 159–194 (2006)Google Scholar
  44. 44.
    Smith, C.: The Parameter of Aspect. Kluwer, Dordrecht (1991)CrossRefGoogle Scholar
  45. 45.
    Verkuyl, H.J.: Aspectual composition: surveying the ingredients. In: Verkuyl, H.J., de Swart, H., van Hout, A. (eds.) Perspectives on Aspect. SITP, vol. 32, pp. 19–39. Springer, Dordrecht (2005).  https://doi.org/10.1007/1-4020-3232-3_2CrossRefGoogle Scholar
  46. 46.
    Pustejovsky, J.: The syntax of event structure. Cognition 41, 47–81 (1991)CrossRefGoogle Scholar
  47. 47.
    Bach, E.: The algebra of events. Linguist. Philos. 9, 5–16 (1986)Google Scholar
  48. 48.
    Jackendoff, R.: Semantic Interpretation in Generative Grammar. MIT press, Cambridge (1972)Google Scholar
  49. 49.
    Lakoff, G.: Irregularity in Syntax. Rinehart and Winstons, New York (1970)Google Scholar
  50. 50.
    Von Wright, G.H.: Norm and Action. Routledge and Kegan Paul, London (1963)Google Scholar
  51. 51.
    Jackendoff, R.: Semantics and Cognition. MIT press, Cambridge (1983)Google Scholar
  52. 52.
    Levin, B., Rapoport, T.R.: Lexical subordination. In: Papers from the 24th Regional Meeting of the Chicago Linguistic Society, pp. 275–289 (1992)Google Scholar
  53. 53.
    Pulman, S.G.: Aspectual shift as type coercion. Trans. Phil. Soc. 95(2), 279–317 (1997)CrossRefGoogle Scholar
  54. 54.
    Hirschfeld, G., Bien, H., de Vries, M., Lüttmann, H., Schwall, J.: Open-source software to conduct online rating studies. Behav. Res. Methods 42, 542–546 (2010)CrossRefGoogle Scholar
  55. 55.
    Salton, G., Wong, A., Yang, C.: A vector space model for automatic indexing. Commun. ACM 18, 613–620 (1975)CrossRefGoogle Scholar
  56. 56.
    Schumacher, H.: Verben in Feldern: Valenzwörterbuch zur Syntax und Semantik deutscher Verben, vol. 1. Walter de Gruyter, Berlin (1986)CrossRefGoogle Scholar
  57. 57.
    Faaß, G., Eckart, K.: SdeWaC – a corpus of parsable sentences from the web. In: Gurevych, I., Biemann, C., Zesch, T. (eds.) GSCL 2013. LNCS (LNAI), vol. 8105, pp. 61–68. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-40722-2_6CrossRefGoogle Scholar
  58. 58.
    Hermes, J., Schwiebert, S.: Classification of text processing components: the tesla role system. In: Fink, A., Lausen, B., Seidel, W., Ultsch, A. (eds.) Advances in Data Analysis, Data Handling and Business Intelligence. STUDIES CLASS, pp. 285–294. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-01044-6_26CrossRefGoogle Scholar
  59. 59.
    Levy, J.P., Bullinaria, J.A.: Learning lexical properties from word usage patterns: which context words should be used? In: French, R.M., Sougné, J.P. (eds.) Connectionist Models of Learning, Development and Evolution. PERSPECT.NEURAL, pp. 273–282. Springer, London (2001).  https://doi.org/10.1007/978-1-4471-0281-6_27CrossRefGoogle Scholar
  60. 60.
    Lund, K., Burgess, C.: Hyperspace analogue to language (HAL): a general model semantic representation. In: Brain and Cognition, vol. 30, p. 265 (1996)Google Scholar
  61. 61.
    Lowe, W., McDonald, S.: The direct route: mediated priming in semantic space. In: Proceedings of the Annual Conference of the Cognitive Science Society (CogSci 2000) (2000)Google Scholar
  62. 62.
    Bohnet, B.: Very high accuracy and fast dependency parsing is not a contradiction. In: Proceedings of the 23rd International Conference on Computational Linguistics, COLING 2010, Stroudsburg, PA, USA, pp. 89–97. Association for Computational Linguistics (2010)Google Scholar
  63. 63.
    Shimodaira, H.: Approximately unbiased tests of region using multistep-mulitscale bootstrap resampling. Ann. Stat. 32, 2616–2641 (2004)MathSciNetCrossRefGoogle Scholar
  64. 64.
    Suzuki, R., Shimodaira, H.: Pvclust: an R package for assessing the uncertainty in hierarchical clustering. Bioinformatics 22, 1540–1542 (2006)CrossRefGoogle Scholar
  65. 65.
    Brooks, P., Tomasello, M.: How children constrain their argument structure constructions. Language 75, 720–738 (1999)CrossRefGoogle Scholar
  66. 66.
    Brooks, P., Tomasello, M., Dodson, K., Lewis, L.: Young children’s overgeneralizations with fixed transitivity verbs. Child Dev. 70, 1325–1337 (1999)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Michael Richter
    • 1
    Email author
  • Jürgen Hermes
    • 2
  • Claes Neuefeind
    • 2
  1. 1.Department of Automatic Language ProcessingLeipzig UniversityLeipzigGermany
  2. 2.Institute for Digital Humanities, Cologne University, Albertus-Magnus-PlatzCologneGermany

Personalised recommendations